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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems Journal of Fuzzy Systems and Control (JFSC)
Puttasakul, Tasawan
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Mechanical Engineering

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Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications Chotikunnan, Phichitphon; Puttasakul, Tasawan; Chotikunnan, Rawiphon; Panomruttanarug, Benjamas; Sangworasil, Manas; Srisiriwat, Anuchart
Journal of Robotics and Control (JRC) Vol 4, No 3 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i3.17932

Abstract

This study presents a method for enhancing the accuracy of object detection in industrial automation applications using ResNet18-based image segmentation. The objective is to extract object images from the background image accurately and efficiently. The study includes three experiments, RGB to grayscale conversion, single image processing, and dual image processing. The results of the experiments show that dual image processing is superior to both RGB to grayscale conversion and single image processing techniques in accurately identifying object edges, determining CG values, and cutting background images and gripper heads. The program achieved a 100% success rate for objects located in the workpiece tray, while also identifying the color and shape of the object using ResNet-18. However, single image processing may have advantages in certain scenarios with sufficient image information and favorable lighting conditions. Both methods have limitations, and future research could focus on further improvements and optimization of these methods, including separating objects into boxes of each type and converting image coordinate data into robot working area coordinates. Overall, this study provides valuable insights into the strengths and limitations of different object recognition techniques for industrial automation applications.
Hybrid Fuzzy-Expert System Control for Robotic Manipulator Applications Chotikunnan, Phichitphon; Roongprasert, Kittipan; Chotikunnan, Rawiphon; Pititheeraphab, Yutthana; Puttasakul, Tasawan; Wongkamhang, Anantasak; Thongpance, Nuntachai
Journal of Robotics and Control (JRC) Vol. 6 No. 1 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i1.24956

Abstract

This research examines a hybrid fuzzy-expert system for the control of robotic manipulators, integrating the flexibility of fuzzy logic with the analytical decision-making capabilities of expert systems. The hybrid system switches dynamically between triangle membership functions, which facilitate smooth transitions, and trapezoidal membership functions, which efficiently manage sudden step changes. This adaptive technique mitigates the shortcomings of independent fuzzy logic controllers, particularly their inconsistency across varied setpoints. Simulation outcomes demonstrate substantial decreases in overshoot and settling time, as well as enhanced adaptability and flexibility in dynamic settings. A comparison test shows that the hybrid system is better than separate triangular and trapezoidal fuzzy controllers because it chooses the best control strategy based on the setpoint attributes in real time. Although there are occasional compromises in accuracy (IAE and RMSE), the hybrid controller provides balanced performance appropriate for various robotic applications. The results confirm its viability as a dependable option for industrial and medical robots, particularly in applications necessitating precision control and adaptability.
Comparative Analysis of Fuzzy Membership Functions for Step and Smooth Input Tracking in a 3-Axis Robotic Manipulator Chotikunnan, Phichitphon; Chotikunnan, Rawiphon; Pititheeraphab, Yutthana; Puttasakul, Tasawan; Wongkamhang, Anantasak; Thongpance, Nuntachai
Journal of Fuzzy Systems and Control Vol. 3 No. 1 (2025): Vol. 3, No. 1, 2025
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v3i1.278

Abstract

Robotic manipulators are essential in industrial and medical applications, requiring precise control to improve efficiency and reduce errors. This research looks at how well fuzzy logic controllers using Gaussian, generalized bell, triangular, and trapezoidal membership functions can handle step and smooth inputs for a robot system that is meant to move materials. Critical metrics like steady-state values, overshoot, rise time, integral absolute error (IAE), and root mean square error (RMSE) were tested using five different methods. The results showed that both the Gaussian and extended bell functions found a good balance between being stable and being responsive. This made them useful for situations with moderate to high input levels. While triangular functions displayed enhanced responsiveness, they also revealed heightened overshoot. In contrast, trapezoidal functions demonstrated significant stability at high saturation levels, although they had challenges in attaining smooth transitions. These findings highlight the necessity of choosing membership functions according to particular application needs. This study investigates the utilization of hybrid methodologies and adaptive optimization strategies to improve fuzzy control systems. These concepts offer compelling approaches to improve accuracy and resilience in dynamic robotic settings.
Enhancing MG996R Servo Motor Performance Using PSO-Tuned PID and Feedforward Control Chotikunnan, Phichitphon; Pititheeraphab, Yutthana; Angsuwatanakul, Thanate; Prinyakupt, Jaroonrut; Puttasakul, Tasawan; Chotikunnan, Rawiphon; Thongpance, Nuntachai
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1854

Abstract

The aim of this research is to improve the precision of factory-locked MG996R servo motors, which are frequently employed in biomedical and robotic applications. These motors are characterized by the absence of inherent feedback channels and adjustable internal settings. The proposed technique proposes a non-invasive control strategy that utilizes externally obtained feedback to enable closed-loop control without requiring any modifications to the interior circuitry. The scientific contribution consists of the development of an outer-loop PID control framework that has been optimized using Particle Swarm Optimization (PSO) and enhanced with feedforward compensation. By utilizing the inherent potentiometer, this method ensures the preservation of hardware integrity and enables real-time angle feedback. A model fit of 96.94% was achieved by establishing a second-order discrete-time model using MATLAB's System Identification Toolbox. Particle Swarm Optimization (PSO) was employed to optimize PID improvements offline by minimizing the Integral of Squared Error (ISE). In both experimental and simulated environments, the controller's effectiveness was assessed using 2 rad/s sine wave inputs and a 10° step. The PSO-PID with feedforward controller achieved optimal results, achieving an RMSE of 0.5313° and an MAE of 0.1630° in simulations, as well as an MAE of 0.8497° in hardware step response. The requirement for gain scaling in embedded systems was underscored by the instability of the standalone PSO-PID controller. This method offers a pragmatic, scalable solution for applications such as assistive robotics, prosthetic joints, and surgical instruments. In order to achieve sub-degree precision in safety-critical environments, future endeavors will entail the implementation of adaptive gain tuning and enhanced resolution sensing.
Noise-Reduced 3D Organ Modeling from CT Images Using Median Filtering for Anatomical Preservation in Medical 3D Printing Chotikunnan, Phichitphon; Chotikunnan, Rawiphon; Puttasakul, Tasawan; Khotakham, Wanida; Imura, Pariwat; Prinyakupt, Jaroonrut; Thongpance, Nuntachai; Srisiriwat, Anuchart
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26665

Abstract

This study offers a systematic approach to improving the reconstruction of three-dimensional anatomical models from CT imaging data. The main difficulty tackled is the maintenance of internal bone features during denoising, essential for producing clinically relevant models. A nonlinear filtering strategy was implemented, utilizing a 3×3 median filter alongside manual refinement to eliminate salt-and-pepper noise while preserving anatomical information. The study presents a reproducible image-processing pipeline that improves structural clarity and enables material-efficient 3D printing while preserving internal bone integrity. A publicly available dataset including 813 anonymized chest CT scans (512×512 pixels, 16-bit grayscale) from Zenodo was employed. Preprocessing included grayscale normalization, brightness adjustment, and the application of median filters with kernel sizes from 3×3 to 9×9, followed by artifact removal using FlashPrint software before STL conversion. The 3×3 median filter achieved an excellent balance between noise reduction and anatomical clarity, outperforming mean filtering and larger kernels in maintaining edge detail. Although statistical evaluation was not conducted, visual analysis validated an 18.07 percent decrease in print time and a 17.88 percent reduction in filament consumption. The technology exhibited actual efficacy in generating high-quality anatomical models. Future endeavors will incorporate automated segmentation and sophisticated denoising methodologies to enhance applicability in surgical simulation, clinical education, and personalized healthcare planning.
Co-Authors Angsuwatanakul, Thanate Chotikunnan, Phichitphon Chotikunnan, Rawiphon Imura, Pariwat Khotakham, Wanida Panomruttanarug, Benjamas Pititheeraphab, Yutthana Prinyakupt, Jaroonrut Roongprasert, Kittipan Sangworasil, Manas Srisiriwat, Anuchart Thongpance, Nuntachai Wongkamhang, Anantasak